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Koelsch N, Mirshahi F, Aqbi HF, Saneshaw M, Idowu MO, Olex AL, Sanyal AJ, Manjili MH. The crosstalking immune cells network creates a collective function beyond the function of each cellular constituent during the progression of hepatocellular carcinoma. Sci Rep 2023; 13:12630. [PMID: 37537225 PMCID: PMC10400568 DOI: 10.1038/s41598-023-39020-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
Abundance of data on the role of inflammatory immune responses in the progression or inhibition of hepatocellular carcinoma (HCC) has failed to offer a curative immunotherapy for HCC. This is largely because of focusing on detailed specific cell types and missing the collective function of the hepatic immune system. To discover the collective immune function, we take systems immunology approach by performing high-throughput analysis of snRNAseq data collected from the liver of DIAMOND mice during the progression of nonalcoholic fatty liver disease (NAFLD) to HCC. We report that mutual signaling interactions of the hepatic immune cells in a dominant-subdominant manner, as well as their interaction with structural cells shape the immunological pattern manifesting a collective function beyond the function of the cellular constituents. Such pattern discovery approach recognized direct role of the innate immune cells in the progression of NASH and HCC. These data suggest that discovery of the immune pattern not only detects the immunological mechanism of HCC in spite of dynamic changes in immune cells during the course of disease but also offers immune modulatory interventions for the treatment of NAFLD and HCC.
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Affiliation(s)
- Nicholas Koelsch
- Department of Microbiology & Immunology, Virginia Commonwealth University School of Medicine, Richmond, VA, 23298, USA.
| | - Faridoddin Mirshahi
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA, 23298, USA
| | - Hussein F Aqbi
- College of Science, Mustansiriyah University, P.O. Box 14022, Baghdad, Iraq
| | - Mulugeta Saneshaw
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA, 23298, USA
| | - Michael O Idowu
- Department of Pathology, VCU School of Medicine, Richmond, VA, 23298, USA
- Department of Microbiology & Immunology, VCU Massey Cancer Center, 401 College Street, Box 980035, Richmond, VA, 23298, USA
| | - Amy L Olex
- C. Kenneth and Dianne Wright Center for Clinical and Translational Research, Virginia Commonwealth University School of Medicine, Richmond, USA
| | - Arun J Sanyal
- Department of Internal Medicine, VCU School of Medicine, Richmond, VA, 23298, USA.
- Department of Microbiology & Immunology, VCU Massey Cancer Center, 401 College Street, Box 980035, Richmond, VA, 23298, USA.
| | - Masoud H Manjili
- Department of Microbiology & Immunology, Virginia Commonwealth University School of Medicine, Richmond, VA, 23298, USA.
- Department of Microbiology & Immunology, VCU Massey Cancer Center, 401 College Street, Box 980035, Richmond, VA, 23298, USA.
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Naghizadeh MM, Bakhshandeh B, Noorbakhsh F, Yaghmaie M, Masoudi-Nejad A. Rewiring of miRNA-mRNA bipartite co-expression network as a novel way to understand the prostate cancer related players. Syst Biol Reprod Med 2023:1-12. [PMID: 37018429 DOI: 10.1080/19396368.2023.2187268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
The differential expression and direct targeting of mRNA by miRNA are two main logics of the traditional approach to constructing the miRNA-mRNA network. This approach, could be led to the loss of considerable information and some challenges of direct targeting. To avoid these problems, we analyzed the rewiring network and constructed two miRNA-mRNA expression bipartite networks for both normal and primary prostate cancer tissue obtained from PRAD-TCGA. We then calculated beta-coefficient of the regression-model when miR was dependent and mRNA independent for each miR and mRNA and separately in both networks. We defined the rewired edges as a significant change in the regression coefficient between normal and cancer states. The rewired nodes through multinomial distribution were defined and network from rewired edges and nodes was analyzed and enriched. Of the 306 rewired edges, 112(37%) were new, 123(40%) were lost, 44(14%) were strengthened, and 27(9%) weakened connections were discovered. The highest centrality of 106 rewired mRNAs belonged to PGM5, BOD1L1, C1S, SEPG, TMEFF2, and CSNK2A1. The highest centrality of 68 rewired miRs belonged to miR-181d, miR-4677, miR-4662a, miR-9.3, and miR-1301. SMAD and beta-catenin binding were enriched as molecular functions. The regulation was a frequently repeated concept in the biological process. Our rewiring analysis highlighted the impact of β-catenin and SMAD signaling as also some transcript factors like TGFB1I1 in prostate cancer progression. Altogether, we developed a miRNA-mRNA co-expression bipartite network to identify the hidden aspects of the prostate cancer mechanism, which traditional analysis -like differential expression- was not detect it.
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Affiliation(s)
- Mohammad Mehdi Naghizadeh
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Behnaz Bakhshandeh
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
| | - Farshid Noorbakhsh
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Marjan Yaghmaie
- Hematology, Oncology and Stem Cell Transplantation Research Center, Institute for Oncology, Hematology and Cell Therapy, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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3
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Progress in and Opportunities for Applying Information Theory to Computational Biology and Bioinformatics. ENTROPY 2022; 24:e24070925. [PMID: 35885148 PMCID: PMC9323281 DOI: 10.3390/e24070925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/27/2022] [Accepted: 06/30/2022] [Indexed: 11/25/2022]
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Moravveji SS, Khoshbakht S, Mokhtari M, Salimi M, Lanjanian H, Nematzadeh S, Torkamanian-Afshar M, Masoudi-Nejad A. Impact of 5HydroxyMethylCytosine (5hmC) on reverse/direct association of cell-cycle, apoptosis, and extracellular matrix pathways in gastrointestinal cancers. BMC Genom Data 2022; 23:49. [PMID: 35768769 PMCID: PMC9241275 DOI: 10.1186/s12863-022-01061-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 06/09/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Aberrant levels of 5-hydroxymethylcytosine (5-hmC) can lead to cancer progression. Identification of 5-hmC-related biological pathways in cancer studies can produce better understanding of gastrointestinal (GI) cancers. We conducted a network-based analysis on 5-hmC levels extracted from circulating free DNAs (cfDNA) in GI cancers including colon, gastric, and pancreatic cancers, and from healthy donors. The co-5-hmC network was reconstructed using the weighted-gene co-expression network method. The cancer-related modules/subnetworks were detected. Preservation of three detected 5-hmC-related modules was assessed in an external dataset. The 5-hmC-related modules were functionally enriched, and biological pathways were identified. The relationship between modules was assessed using the Pearson correlation coefficient (p-value < 0.05). An elastic network classifier was used to assess the potential of the 5-hmC modules in distinguishing cancer patients from healthy individuals. To assess the efficiency of the model, the Area Under the Curve (AUC) was computed using five-fold cross-validation in an external dataset. RESULTS The main biological pathways were the cell cycle, apoptosis, and extracellular matrix (ECM) organization. Direct association between the cell cycle and apoptosis, inverse association between apoptosis and ECM organization, and inverse association between the cell cycle and ECM organization were detected for the 5-hmC modules in GI cancers. An AUC of 92% (0.73-1.00) was observed for the predictive model including 11 genes. CONCLUSION The intricate association between biological pathways of identified modules may reveal the hidden significance of 5-hmC in GI cancers. The identified predictive model and new biomarkers may be beneficial in cancer detection and precision medicine using liquid biopsy in the early stages.
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Affiliation(s)
- Sayyed Sajjad Moravveji
- Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | - Samane Khoshbakht
- Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | - Majid Mokhtari
- Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran
| | - Mahdieh Salimi
- Department of Medical Genetics, Institute of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Hossein Lanjanian
- Molecular Biology and Genetics Department, Engineering and Natural Science Faculty, Istinye University, Istanbul, Turkey
| | - Sajjad Nematzadeh
- Computer Engineering Department, Architecture and Engineering Faculty, Nisantasi University, Istanbul, Turkey
| | - Mahsa Torkamanian-Afshar
- Computer Engineering Department, Architecture and Engineering Faculty, Nisantasi University, Istanbul, Turkey
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Department of Bioinformatics, Kish International Campus, University of Tehran, Kish Island, Iran.
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Fathinavid A, Mousavian Z, Najafi A, Nematzadeh S, Salimi M, Masoudi-Nejad A. Identifying common signatures and potential therapeutic biomarkers in COPD and lung cancer using miRNA-mRNA co-expression networks. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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Bussi Y, Kapon R, Reich Z. Large-scale k-mer-based analysis of the informational properties of genomes, comparative genomics and taxonomy. PLoS One 2021; 16:e0258693. [PMID: 34648558 PMCID: PMC8516232 DOI: 10.1371/journal.pone.0258693] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 10/02/2021] [Indexed: 12/24/2022] Open
Abstract
Information theoretic approaches are ubiquitous and effective in a wide variety of bioinformatics applications. In comparative genomics, alignment-free methods, based on short DNA words, or k-mers, are particularly powerful. We evaluated the utility of varying k-mer lengths for genome comparisons by analyzing their sequence space coverage of 5805 genomes in the KEGG GENOME database. In subsequent analyses on four k-mer lengths spanning the relevant range (11, 21, 31, 41), hierarchical clustering of 1634 genus-level representative genomes using pairwise 21- and 31-mer Jaccard similarities best recapitulated a phylogenetic/taxonomic tree of life with clear boundaries for superkingdom domains and high subtree similarity for named taxons at lower levels (family through phylum). By analyzing ~14.2M prokaryotic genome comparisons by their lowest-common-ancestor taxon levels, we detected many potential misclassification errors in a curated database, further demonstrating the need for wide-scale adoption of quantitative taxonomic classifications based on whole-genome similarity.
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Affiliation(s)
- Yuval Bussi
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ruti Kapon
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Ziv Reich
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
- * E-mail:
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Hameduh T, Mokry M, Miller AD, Adam V, Heger Z, Haddad Y. A rotamer relay information system in the epidermal growth factor receptor-drug complexes reveals clues to new paradigm in protein conformational change. Comput Struct Biotechnol J 2021; 19:5443-5454. [PMID: 34667537 PMCID: PMC8511715 DOI: 10.1016/j.csbj.2021.09.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 09/13/2021] [Accepted: 09/24/2021] [Indexed: 11/04/2022] Open
Abstract
Cancer cells can escape the effects of chemotherapy through mutations and upregulation of a tyrosine kinase protein called the epidermal growth factor receptor (EGFR). In the past two decades, four generations of tyrosine kinase inhibitors targeting EGFR have been developed. Using comparative structure analysis of 116 EGFR-drug complex crystal structures, cluster analysis produces two clans of 73 and 43 structures, respectively. The first clan of 73 structures is larger and is comprised mostly of the C-helix-IN conformation while the second clan of 43 structures correlates with the C-helix-OUT conformation. A deep rotamer analysis identifies 43 residues (18%) of the total of 237 residues spanning the kinase structures under investigation with significant rotamer variations between the C-helix-IN and C-helix-OUT clans. The locations of these rotamer variations take on the appearance of side chain conformational relays extending out from points of EGFR mutation to different regions of the EGFR kinase. Accordingly, we propose that key EGFR mutations act singly or together to induce drug resistant conformational changes in EGFR that are communicated via these side chain conformational relays. Accordingly, these side chain conformational relays appear to play a significant role in the development of tumour resistance. This phenomenon also suggests a new paradigm in protein conformational change that is mediated by supportive relays of rotamers on the protein surface, rather than through conventional backbone movements.
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Affiliation(s)
- Tareq Hameduh
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
| | - Michal Mokry
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Andrew D. Miller
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Veterinary Research Institute, Hudcova 70, CZ-62100 Brno, Czech Republic
- KP Therapeutics (Europe) s.r.o., Purkyňova 649/127, Brno CZ-61200, Czech Republic
| | - Vojtech Adam
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Zbynek Heger
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Yazan Haddad
- Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
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8
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Pournoor E, Mousavian Z, Nowzari-Dalini A, Masoudi-Nejad A. A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma. PLoS One 2021; 16:e0255718. [PMID: 34370784 PMCID: PMC8351981 DOI: 10.1371/journal.pone.0255718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 07/22/2021] [Indexed: 11/19/2022] Open
Abstract
Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.
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Affiliation(s)
- Ehsan Pournoor
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Zaynab Mousavian
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Abbas Nowzari-Dalini
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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9
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Tran TD, Pham DT. Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks. Sci Rep 2021; 11:14095. [PMID: 34238960 PMCID: PMC8266823 DOI: 10.1038/s41598-021-93336-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 06/23/2021] [Indexed: 12/16/2022] Open
Abstract
Each cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.
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Affiliation(s)
- Tien-Dzung Tran
- Complex Systems and Bioinformatics Lab, Faculty of Information and Communication Technology, Hanoi University of Industry, Bac Tu Liem District, 298 Cau Dien street, Hanoi, Vietnam. .,Department of Software Engineering, Faculty of Information and Communication Technology, Hanoi University of Industry, Bac Tu Liem District, 298 Cau Dien street, Hanoi, Vietnam.
| | - Duc-Tinh Pham
- Complex Systems and Bioinformatics Lab, Faculty of Information and Communication Technology, Hanoi University of Industry, Bac Tu Liem District, 298 Cau Dien street, Hanoi, Vietnam.,Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
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10
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Ebrahimi A, Yousefi M, Shahbazi F, Sheikh Beig Goharrizi MA, Masoudi-Nejad A. Nodes with the highest control power play an important role at the final level of cooperation in directed networks. Sci Rep 2021; 11:13668. [PMID: 34211043 PMCID: PMC8249622 DOI: 10.1038/s41598-021-93144-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
Controllability of complex networks aims to seek the lowest number of nodes (the driver nodes) that can control all the nodes by receiving the input signals. The concept of control centrality is used to determine the power of each node to control the network. The more a node controls the nodes through connections in the network, the more it has the power to control. Although the cooperative and free-rider strategies and the final level of cooperation in a population are considered and studied in the public goods game. However, it is yet to determine a solution to indicate the effectiveness of each member in changing the strategies of the other members. In a network, the choice of nodes effective in changing the other nodes' strategies, as free-riders, will lead to lower cooperation and vice versa. This paper uses simulated and real networks to investigate that the nodes with the highest control power are more effective than the hubs, local, and random nodes in changing the strategies of the other nodes and the final level of cooperation. Results indicate that the nodes with the highest control power as free-riders, compared to the other sets being under consideration, can lead to a lower level of cooperation and are, therefore, more effective in changing the strategies of the other nodes. The obtained results can be considered in the treatment of cancer. So that, destroying the tumoral cells with the highest control power should be a priority as these cells have a higher capability to change the strategies of the other cells from cooperators to free-riders (healthy to tumoral).
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Affiliation(s)
- Ali Ebrahimi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Marzieh Yousefi
- Department of Physics, Isfahan University of Technology (IUT), Isfahan, Iran
| | - Farhad Shahbazi
- Department of Physics, Isfahan University of Technology (IUT), Isfahan, Iran
| | | | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Miyagi H, Hiroshima M, Sako Y. Cell-to-cell diversification in ERBB-RAS-MAPK signal transduction that produces cell-type specific growth factor responses. Biosystems 2020; 199:104293. [PMID: 33221378 DOI: 10.1016/j.biosystems.2020.104293] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 11/12/2020] [Accepted: 11/14/2020] [Indexed: 02/06/2023]
Abstract
Growth factors regulate cell fates, including their proliferation, differentiation, survival, and death, according to the cell type. Even when the response to a specific growth factor is deterministic for collective cell behavior, significant levels of fluctuation are often observed between single cells. Statistical analyses of single-cell responses provide insights into the mechanism of cell fate decisions but very little is known about the distributions of the internal states of cells responding to growth factors. Using multi-color immunofluorescent staining, we have here detected the phosphorylation of seven elements in the early response of the ERBB-RAS-MAPK system to two growth factors. Among these seven elements, five were analyzed simultaneously in distinct combinations in the same single cells. Although principle component analysis suggested cell-type and input specific phosphorylation patterns, cell-to-cell fluctuation was large. Mutual information analysis suggested that each cell type uses multitrack (bush-like) signal transduction pathways under conditions in which clear fate changes have been reported. The clustering of single-cell response patterns indicated that the fate change in a cell population correlates with the large entropy of the response, suggesting a bet-hedging strategy is used in decision making. A comparison of true and randomized datasets further indicated that this large variation is not produced by simple reaction noise, but is defined by the properties of the signal-processing network.
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Affiliation(s)
- Hiraku Miyagi
- Cellular Informatics Laboratory, RIKEN, Cluster for Pioneering Research, 2-1, Hirosawa, Wako, 351-0198, Japan; CREST, JST, 4-1-8, Honcho, Kawaguchi, 332-0012, Japan
| | - Michio Hiroshima
- Cellular Informatics Laboratory, RIKEN, Cluster for Pioneering Research, 2-1, Hirosawa, Wako, 351-0198, Japan; CREST, JST, 4-1-8, Honcho, Kawaguchi, 332-0012, Japan; Laboratory for Cell Signaling Dynamics, RIKEN, Center for Biosystems Dynamics Research, 6-2-3, Furuedai, Suita, 565-0874, Japan
| | - Yasushi Sako
- Cellular Informatics Laboratory, RIKEN, Cluster for Pioneering Research, 2-1, Hirosawa, Wako, 351-0198, Japan; CREST, JST, 4-1-8, Honcho, Kawaguchi, 332-0012, Japan.
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12
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Ostovar G, Naughton KL, Boedicker JQ. Computation in bacterial communities. Phys Biol 2020; 17:061002. [PMID: 33035198 DOI: 10.1088/1478-3975/abb257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Bacteria across many scales are involved in a dynamic process of information exchange to coordinate activity and community structure within large and diverse populations. The molecular components bacteria use to communicate have been discovered and characterized, and recent efforts have begun to understand the potential for bacterial signal exchange to gather information from the environment and coordinate collective behaviors. Such computations made by bacteria to coordinate the action of a population of cells in response to information gathered by a multitude of inputs is a form of collective intelligence. These computations must be robust to fluctuations in both biological, chemical, and physical parameters as well as to operate with energetic efficiency. Given these constraints, what are the limits of computation by bacterial populations and what strategies have evolved to ensure bacterial communities efficiently work together? Here the current understanding of information exchange and collective decision making that occur in microbial populations will be reviewed. Looking toward the future, we consider how a deeper understanding of bacterial computation will inform future direction in microbiology, biotechnology, and biophysics.
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Affiliation(s)
- Ghazaleh Ostovar
- Department of Physics and Astronomy, University of Southern California, Los Angeles, CA 90089, United States of America
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13
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Tan YY, Montagnese S, Mani AR. Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure. Front Physiol 2020; 11:983. [PMID: 32848892 PMCID: PMC7422730 DOI: 10.3389/fphys.2020.00983] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 07/20/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND A healthy individual has a high degree of functional connectivity between organ systems, which can be represented graphically in a network map. Disruption of this system connectivity is associated with mortality in life-threatening acute illnesses, demonstrated by a network approach. However, this approach has not been applied to chronic multisystem diseases and may be more reliable than conventional individual organ prognostic scoring methods. Cirrhosis is a chronic disease of the liver with multisystem involvement. Development of an efficient model for prediction of mortality in cirrhosis requires a profound understanding of the pathophysiologic processes that lead to poor prognosis. In the present study, we use a network approach to evaluate the differences in organ system connectivity between survivors and non-survivors in a group of well-characterized patients with cirrhosis. METHODS 201 patients with cirrhosis originally referred to the Clinic five at the University Hospital of Padova, with 13 clinical variables available representing hepatic, metabolic, haematopoietic, immune, neural and renal organ systems were retrospectively enrolled and followed up for 3, 6, and 12 months. Software was designed to compute the correlation network maps of organ system interaction in survivors and non-survivors using analysis indices: A. Bonferroni corrected Pearson's correlation coefficient and B. Mutual Information. Network structure was quantitatively evaluated using the measures of edges, average degree of connectivity and closeness, and qualitatively using clinical significance. Pair-matching was also implemented as a further step after initial general analysis to control for sample size and Model for End-Stage Liver Disease (MELD-Na) score between the groups. RESULTS There was a higher number of significant correlations in survivors, as indicated by both the analysis indices of Bonferroni corrected Pearson's correlation coefficient and the Mutual Information analysis. The number of edges, average degree of connectivity and closeness were significantly higher in survivors compared to non-survivors group. Pair-matching for MELD-Na was also associated with increased connectivity in survivors compared to non-survivors over 3 and 6 months follow up. CONCLUSION This study demonstrates the application of a network approach in evaluating functional connectivity of organ systems in liver cirrhosis, demonstrating a significant degree of network disruption in organ systems in non-survivors. Network analysis of organ systems may provide insight in developing novel prognostic models for organ allocation in patients with cirrhosis.
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Affiliation(s)
- Yen Yi Tan
- Network Physiology Laboratory, UCL Division of Medicine, University College London, London, United Kingdom
| | | | - Ali R. Mani
- Network Physiology Laboratory, UCL Division of Medicine, University College London, London, United Kingdom
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Chanda P, Costa E, Hu J, Sukumar S, Van Hemert J, Walia R. Information Theory in Computational Biology: Where We Stand Today. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E627. [PMID: 33286399 PMCID: PMC7517167 DOI: 10.3390/e22060627] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 05/31/2020] [Accepted: 06/03/2020] [Indexed: 12/30/2022]
Abstract
"A Mathematical Theory of Communication" was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon's work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology-gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.
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Affiliation(s)
- Pritam Chanda
- Corteva Agriscience™, Indianapolis, IN 46268, USA
- Computer and Information Science, Indiana University-Purdue University, Indianapolis, IN 46202, USA
| | - Eduardo Costa
- Corteva Agriscience™, Mogi Mirim, Sao Paulo 13801-540, Brazil
| | - Jie Hu
- Corteva Agriscience™, Indianapolis, IN 46268, USA
| | | | | | - Rasna Walia
- Corteva Agriscience™, Johnston, IA 50131, USA
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15
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Biological Network Approaches and Applications in Rare Disease Studies. Genes (Basel) 2019; 10:genes10100797. [PMID: 31614842 PMCID: PMC6827097 DOI: 10.3390/genes10100797] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 09/30/2019] [Accepted: 10/10/2019] [Indexed: 12/26/2022] Open
Abstract
Network biology has the capability to integrate, represent, interpret, and model complex biological systems by collectively accommodating biological omics data, biological interactions and associations, graph theory, statistical measures, and visualizations. Biological networks have recently been shown to be very useful for studies that decipher biological mechanisms and disease etiologies and for studies that predict therapeutic responses, at both the molecular and system levels. In this review, we briefly summarize the general framework of biological network studies, including data resources, network construction methods, statistical measures, network topological properties, and visualization tools. We also introduce several recent biological network applications and methods for the studies of rare diseases.
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16
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Parag KV. On signalling and estimation limits for molecular birth-processes. J Theor Biol 2019; 480:262-273. [PMID: 31299332 DOI: 10.1016/j.jtbi.2019.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 07/05/2019] [Accepted: 07/09/2019] [Indexed: 12/14/2022]
Abstract
Understanding and uncovering the mechanisms or motifs that molecular networks employ to regulate noise is a key problem in cell biology. As it is often difficult to obtain direct and detailed insight into these mechanisms, many studies instead focus on assessing the best precision attainable on the signalling pathways that compose these networks. Molecules signal one another over such pathways to solve noise regulating estimation and control problems. Quantifying the maximum precision of these solutions delimits what is achievable and allows hypotheses about underlying motifs to be tested without requiring detailed biological knowledge. The pathway capacity, which defines the maximum rate of transmitting information along it, is a widely used proxy for precision. Here it is shown, for estimation problems involving elementary yet biologically relevant birth-process networks, that capacity can be surprisingly misleading. A time-optimal signalling motif, called birth-following, is derived and proven to better the precision expected from the capacity, provided the maximum signalling rate constraint is large and the mean one above a certain threshold. When the maximum constraint is relaxed, perfect estimation is predicted by the capacity. However, the true achievable precision is found highly variable and sensitive to the mean constraint. Since the same capacity can map to different combinations of rate constraints, it can only equivocally measure precision. Deciphering the rate constraints on a signalling pathway may therefore be more important than computing its capacity.
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Affiliation(s)
- Kris V Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, W2 1PG London.
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17
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Sai A, Kong N. Exploring the information transmission properties of noise-induced dynamics: application to glioma differentiation. BMC Bioinformatics 2019; 20:375. [PMID: 31272368 PMCID: PMC6610902 DOI: 10.1186/s12859-019-2970-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 06/26/2019] [Indexed: 12/21/2022] Open
Abstract
Background Cells operate in an uncertain environment, where critical cell decisions must be enacted in the presence of biochemical noise. Information theory can measure the extent to which such noise perturbs normal cellular function, in which cells must perceive environmental cues and relay signals accurately to make timely and informed decisions. Using multivariate response data can greatly improve estimates of the latent information content underlying important cell fates, like differentiation. Results We undertake an information theoretic analysis of two stochastic models concerning glioma differentiation therapy, an alternative cancer treatment modality whose underlying intracellular mechanisms remain poorly understood. Discernible changes in response dynamics, as captured by summary measures, were observed at low noise levels. Mitigating certain feedback mechanisms present in the signaling network improved information transmission overall, as did targeted subsampling and clustering of response dynamics. Conclusion Computing the channel capacity of noisy signaling pathways present great probative value in uncovering the prevalent trends in noise-induced dynamics. Areas of high dynamical variation can provide concise snapshots of informative system behavior that may otherwise be overlooked. Through this approach, we can examine the delicate interplay between noise and information, from signal to response, through the observed behavior of relevant system components. Electronic supplementary material The online version of this article (10.1186/s12859-019-2970-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Aditya Sai
- Weldon School of Biomedical Engineering, Purdue University, 206 S Martin Jischke Drive, West Lafayette, 47907, IN, USA.
| | - Nan Kong
- Weldon School of Biomedical Engineering, Purdue University, 206 S Martin Jischke Drive, West Lafayette, 47907, IN, USA
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18
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Root A. Do cells use passwords in cell-state transitions? Is cell signaling sometimes encrypted? Theory Biosci 2019; 139:87-93. [PMID: 31175621 DOI: 10.1007/s12064-019-00295-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 06/03/2019] [Indexed: 11/28/2022]
Abstract
Organisms must maintain proper regulation including defense and healing. Life-threatening problems may be caused by pathogens or by a multicellular organism's own cells through cancer or autoimmune disorders. Life evolved solutions to these problems that can be conceptualized through the lens of information security, which is a well-developed field in computer science. Here I argue that taking an information security view of cells is not merely semantics, but useful to explain features of signaling, regulation, and defense. An information security perspective also offers a conduit for cross-fertilization of advanced ideas from computer science and the potential for biology to inform computer science. First, I consider whether cells use passwords, i.e., initiation sequences that are required for subsequent signals to have effects, by analyzing the concept of pioneer transcription factors in chromatin regulation and cellular reprogramming. Second, I consider whether cells may encrypt signal transduction cascades. Encryption could benefit cells by making it more difficult for pathogens or oncogenes to hijack cell networks. By using numerous molecules, cells may gain a security advantage in particular against viruses, whose genome sizes are typically under selection pressure. I provide a simple conceptual argument for how cells may perform encryption through posttranslational modifications, complex formation, and chromatin accessibility. I invoke information theory to provide a criterion of an entropy spike to assess whether a signaling cascade has encryption-like features. I discuss how the frequently invoked concept of context dependency may oversimplify more advanced features of cell signaling networks, such as encryption. Therefore, by considering that biochemical networks may be even more complex than commonly realized we may be better able to understand defenses against pathogens and pathologies.
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Affiliation(s)
- Alex Root
- Molecular Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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19
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Billing U, Jetka T, Nortmann L, Wundrack N, Komorowski M, Waldherr S, Schaper F, Dittrich A. Robustness and Information Transfer within IL-6-induced JAK/STAT Signalling. Commun Biol 2019; 2:27. [PMID: 30675525 PMCID: PMC6338669 DOI: 10.1038/s42003-018-0259-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Accepted: 12/07/2018] [Indexed: 01/06/2023] Open
Abstract
Cellular communication via intracellular signalling pathways is crucial. Expression and activation of signalling proteins is heterogenous between isogenic cells of the same cell-type. However, mechanisms evolved to enable sufficient communication and to ensure cellular functions. We use information theory to clarify mechanisms facilitating IL-6-induced JAK/STAT signalling despite cell-to-cell variability. We show that different mechanisms enabling robustness against variability complement each other. Early STAT3 activation is robust as long as cytokine concentrations are low. Robustness at high cytokine concentrations is ensured by high STAT3 expression or serine phosphorylation. Later the feedback-inhibitor SOCS3 increases robustness. Channel Capacity of JAK/STAT signalling is limited by cell-to-cell variability in STAT3 expression and is affected by the same mechanisms governing robustness. Increasing STAT3 amount increases Channel Capacity and robustness, whereas increasing STAT3 tyrosine phosphorylation reduces robustness but increases Channel Capacity. In summary, we elucidate mechanisms preventing dysregulated signalling by enabling reliable JAK/STAT signalling despite cell-to-cell heterogeneity.
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Affiliation(s)
- Ulrike Billing
- Otto-von-Guericke University Magdeburg, Institute of Biology, Department of Systems Biology, Universitätsplatz 2, 39106 Magdeburg, Germany
| | - Tomasz Jetka
- Polish Academy of Sciences, Institute of Fundamental Technological Research, Division of Modelling in Biology and Medicine, Pawinskiego 5B, 02- 106, Warszawa, Poland
| | - Lukas Nortmann
- Otto-von-Guericke University Magdeburg, Institute of Biology, Department of Systems Biology, Universitätsplatz 2, 39106 Magdeburg, Germany
| | - Nicole Wundrack
- Otto-von-Guericke University Magdeburg, Institute of Biology, Department of Systems Biology, Universitätsplatz 2, 39106 Magdeburg, Germany
| | - Michal Komorowski
- Polish Academy of Sciences, Institute of Fundamental Technological Research, Division of Modelling in Biology and Medicine, Pawinskiego 5B, 02- 106, Warszawa, Poland
| | - Steffen Waldherr
- KU Leuven, Department of Chemical Engineering, Celestijnenlaan 200f - box 2424, 3001 Leuven, Belgium
| | - Fred Schaper
- Otto-von-Guericke University Magdeburg, Institute of Biology, Department of Systems Biology, Universitätsplatz 2, 39106 Magdeburg, Germany
| | - Anna Dittrich
- Otto-von-Guericke University Magdeburg, Institute of Biology, Department of Systems Biology, Universitätsplatz 2, 39106 Magdeburg, Germany
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20
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Frost JJ, Pienta KJ, Coffey DS. Symmetry and symmetry breaking in cancer: a foundational approach to the cancer problem. Oncotarget 2017; 9:11429-11440. [PMID: 29545909 PMCID: PMC5837760 DOI: 10.18632/oncotarget.22939] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 11/01/2017] [Indexed: 12/27/2022] Open
Abstract
Symmetry and symmetry breaking concepts from physics and biology are applied to the problem of cancer. Three categories of symmetry breaking in cancer are examined: combinatorial, geometric, and functional. Within these categories, symmetry breaking is examined for relevant cancer features, including epithelial-mesenchymal transition (EMT); tumor heterogeneity; tensegrity; fractal geometric and information structure; functional interaction networks; and network stabilizability and attack tolerance. The new cancer symmetry concepts are relevant to homeostasis loss in cancer and to its origin, spread, treatment and resistance. Symmetry and symmetry breaking could provide a new way of thinking and a pathway to a solution of the cancer problem.
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Affiliation(s)
- J James Frost
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kenneth J Pienta
- James Buchanan Brady Urological Institute at the Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Medical Oncology, Johns Hopkins School of Medicine and Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA.,Department of Pharmacology and Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Donald S Coffey
- James Buchanan Brady Urological Institute at the Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Vella D, Zoppis I, Mauri G, Mauri P, Di Silvestre D. From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2017; 2017:6. [PMID: 28477207 PMCID: PMC5359264 DOI: 10.1186/s13637-017-0059-z] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 03/09/2017] [Indexed: 12/19/2022]
Abstract
The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.
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Affiliation(s)
- Danila Vella
- Institute for Biomedical Technologies - National Research Council (ITB-CNR), 93 Fratelli Cervi, Segrate, Milan, Italy.,Department of Computer Science, Systems and Communication DiSCo, University of Milano-Bicocca, 336 Viale Sarca, Milan, Italy
| | - Italo Zoppis
- Department of Computer Science, Systems and Communication DiSCo, University of Milano-Bicocca, 336 Viale Sarca, Milan, Italy
| | - Giancarlo Mauri
- Department of Computer Science, Systems and Communication DiSCo, University of Milano-Bicocca, 336 Viale Sarca, Milan, Italy
| | - Pierluigi Mauri
- Institute for Biomedical Technologies - National Research Council (ITB-CNR), 93 Fratelli Cervi, Segrate, Milan, Italy
| | - Dario Di Silvestre
- Institute for Biomedical Technologies - National Research Council (ITB-CNR), 93 Fratelli Cervi, Segrate, Milan, Italy.
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22
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Iglesias PA. The Use of Rate Distortion Theory to Evaluate Biological Signaling Pathways. ACTA ACUST UNITED AC 2016. [DOI: 10.1109/tmbmc.2016.2623600] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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23
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Masoudi-Nejad A, Zenil H. Flow of Information in Biological Systems. Semin Cell Dev Biol 2016; 51:1-2. [PMID: 26987578 DOI: 10.1016/j.semcdb.2016.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Hector Zenil
- Unit of Computational Medicine, Science for Life Laboratory (SciLifeLab), Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institute, Stockholm, Sweden.
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